It is known that EGM signals are collected by use of electrodes placed on endocardial or epicardial leads of a device implanted in a patient. These signals, directly related to the electrical activities of cardiac cells of the patient, provide much useful information for the purpose of assessing the patient's condition. Hence, after amplifying, conditioning, digitizing and filtering, EGM signals are mainly utilized to control the implanted device and diagnose rhythm disorders requiring, for example, automatic triggering of an antitachycardia, antibradycardia, or interventricular resynchronization therapy.
However, when it comes to analyzing subjectively the cardiac rhythm, e.g., to perform a diagnosis or readjust the control/operating parameters of an implanted device, the practitioners prefer, in practice, to interpret the information given by a surface electrocardiogram (ECG). An ECG allows one to visualize in a direct manner, a certain number of determining factors (e.g., QRS width) and thereby assess the evolution of a cardiac failure.
ECG signals are usually recorded over a long period of time through ambulatory practice by Holter recorders. The recorded ECG signals are then further processed and analyzed in order to evaluate the clinical condition of the patient and eventually diagnose whether a cardiac rhythm disorder is present.
The ECG and EGM signals actually have the same signal source (i.e., the electrical activity of myocardium), however, they visually appear in much different manners: the EGM collected by the implantable device provides local information on the electrical activity of a group of heart cells, whereas the ECG appears in the form of more global information, in particular influenced by the propagation of the electrical signals between the myocardium and body surface, with certain morphologic and pathologic specificities. Thus, the display of EGM signals is not very useful to a practitioner who interprets ECG signals.
When a patient implanted with a medical device comes to his practitioner for a routine visit, two distinct devices are used: an ECG recorder and an external implant programmer. In order to collect the ECG signal, the practitioner places electrodes in particular locations relative to the patient's torso. The ECG signals are collected between predefined pairs of electrodes to define typically twelve “derivations” of the collected ECG signals. The external programmer is used to control certain operating parameters of the implantable device (e.g., the battery life), download data from the implantable device memory, modify the parameters thereof, or upload an updated version of the device operating software, etc.
The visit with the practitioner therefore usually requires these two different devices, as well as specific manipulations for placing the surface electrodes and collecting the ECG signals. Moreover, the use of these two devices requires the patient to come to a specifically equipped center, usually having the consequence that routine visits are spaced farther apart, resulting in a less rigorous follow-up of the patient.
Furthermore, the ECG recording has various drawbacks, notably:                the preparation of the patient which requires a certain time, correlated with a globally increased follow-up cost;        the local irritation of the skin created by fixing of the electrodes in some patients;        the position of the electrodes varies from one visit to another, inducing variations in the reconstructed ECG;        the ECG is affected by several parameters difficult to control, such as breathing, movements of the patient, as well as the interferences emitted by various external electrical sources.        
In order to overcome such drawbacks, some algorithms have been developed for reconstruction of the ECG based upon EGM signals that are directly provided by the implantable device. Indeed, reconstruction of the ECG based upon EGM signals would:                avoid, during routine visits, having to place surface electrodes and resort to an ECG recorder;        render the visit simpler and quicker, eventually allow performing the routine visit at the patient's home, and subsequently shorten the intervals between successive visits, and improve the patient's follow-up; and        allow a remote transmission of the EGM data recorded by the implanted device, without the intervention of a practitioner or medical aid.        
Various algorithms for surface ECG reconstruction based upon EGM signals have been previously proposed. Certain of the techniques implement a linear or non-linear combination of a plurality of EGM signals.
U.S. Pat. No. 5,740,811 (Hedberg, et al.) proposes to synthesize an ECG signal by combining a plurality of EGM signals by means of a neural network and/or fuzzy logic and/or summer circuit, after a learning process by a “feedforward” type algorithm. Such technique do not take into account the propagation time delay between the EGM signals and the surface ECG signals leading to a precision loss in the reconstructed ECG signal. Another drawback of such technique is that it does not take into account the varying position of the endocardial leads between the moment of the learning process and that of the use of the device; a change in the heart electrical axis may bias the synthesized ECG signal, generating a misleading ECG signal. A cardiac disorder that is masked by the biased synthesis may not be accurately diagnosed.
U.S. Pat. No. 6,980,850 (Kroll et al.) proposes a method of ECG reconstruction by implementing a matrix transform allowing to render each of the surface ECG derivations individually. Such transform also allows to take into account several parameters, such as patient's respiratory activity or posture that influence tracking the position of the endocardial leads through space. The proposed reconstruction consists of transforming, through a predetermined transfer matrix, an input vector representative of a plurality of EGM signals into a resulting vector representative of the different ECG derivations. The transfer matrix is learned through averaging several instantaneous matrices based upon ECG and EGM vectors recorded simultaneously over a same period of time.
Although this technique brings an improvement to that proposed in U.S. Pat. No. 5,740,811, it nevertheless presents certain drawbacks. First, it makes an assumption that there exists a linear relationship between ECG and EGM vectors: such an approximation, though relatively accurate with patients presenting a regular rhythm, leads in some cases to large errors of ECG reconstruction in the presence of atypical or irregular signal morphologies—corresponding to potentially pathologic cases. Second, in the presence of noise, it does not provide a solution for appropriately reconstructing the ECG signals.
The U.S. Pat. No. 7,383,080, the EP 1897587 A2 and the U.S. 2008/0065161 describe yet another technique for concatenating a ventricular far field signal (distant signal) observed on an atrium electrode on one hand, with an atrial far field signal (distant signal) observed on a ventricular electrode on the other hand, to reconstruct an ECG signal. By convention, here and in what follows:                “atrial far field” or FFA designates a depolarization signal from a ventricular origin (the ventricular distant “electric noise”) observed on an electrode placed in the atrium during or just after an occurrence of an R wave, and        “ventricular far field” FFV designates a depolarization signal from an atrial origin (the atrial distant “electric noise”) observed on an electrode placed in the ventricle during or just after an occurrence of a P wave.        
This known prior art concatenating technique is illustrated in FIG. 1. On this figure, an atrial far field signal FFA extracted from the atrial EGM signal EGMA over the duration of a time window surrounding a peak R (spontaneous activity having its origin in the ventricle) is represented, this peak being located on the ventricular EGM signal EGMV. Similarly, a ventricular far field signal FFV is extracted from the signal EGMV during a time window surrounding a peak P (spontaneous activity having its origin in the atrium), said peak being located on the atrial EGM signal EGMA.
The reconstructed ECG signal (designated herein as “ECG*”) is obtained by concatenating the segments FFV and FFA after subtraction of an offset and multiplication of each segment by a given factor of amplification or attenuation, so as to connect these segments between them during their concatenation.
This technique presents a number of advantages:                in the case of a patient with a regular heart rhythm, it is effective because the two far field signals are well separated;        it is simple to implement, and can therefore be implemented in real time within an implanted device;        it does not require the collection of an ECG, unlike methods that use linear or nonlinear EGM signals and require a learning phase for determining the coefficients of filters or of transfer matrices; and        it can detect polarity reversals on some ECG signals, while the reconstruction of this polarity reversal is not possible if a linear or nonlinear processing of all EGM signals is performed.        
These advantages are however tempered by a number of drawbacks:                for a patient with an irregular heart rhythm, thus potentially pathological illness, far field signals are hidden in P and R waves and cannot be satisfactorily isolated;        the reconstructed “electrocardiogram” signal does not correspond to any real ECG derivation. The technique provides an emulation of a virtual ECG signal (“ECG-like signal”), and is useful for diagnosis, rather than to realistically reconstruct an ECG signal which, ideally, would reproduce a signal collected on one or several derivations of a surface electrocardiogram; and        this technique can produce only one type of ECG signal, not a plurality of reconstructed ECG signals, similar to what would be obtained during the collection of a conventional surface electrocardiogram.        
These various drawbacks are notably due to the fact the EGM and ECG signals, even if they have the same origin, have very different characteristics. Indeed, the electrical activities of a patient's heart reflect the spontaneous stimulations caused by the ionic currents in the cardiac cells or artificial stimulations produced by the application on an electrical current to these cells. The EGM signals, directly collected by the implant on one or more derivations, reflect the electrical potentials of the myocardium, whereas the ECG signals correspond to the electrical potentials recorded on a body surface, over a certain number of derivations, after propagating from the myocardium.
Another drawback, specific to all these techniques, is that they do not allow verifying that the reconstruction of ECG signals gives a correct result, and do not provide a criterion quantifying the quality of the reconstruction.
It would be desirable to have such a criterion, especially in terms of the intended use of the reconstructed ECG signals: for a simple determination of the presence or absence of a peak, or a QRS complex, the ECG signals are reconstructed with an average quality, while for accurate diagnosis based on specific details of ECG or measurements on this ECG, the ECG signals must be reconstructed with a superior quality.